Bee-8B-SFT-abliterated

Bee-8B-SFT-abliterated is an abliterated (v1.0) variant of Open-Bee’s Bee-8B-SFT model. It is a high-quality supervised fine-tuning (SFT) variant trained on approximately 15 million curated samples. This dataset was meticulously created using HoneyPipe, Open-Bee’s transparent, adaptable, and open-source data curation pipeline that systematically cleans noisy data and enriches it through a dual-level Chain-of-Thought (CoT) strategy for both short and long reasoning contexts.

Key Highlights

• Abliterated / Uncensored Captioning and Reasoning Fine-tuned to bypass standard content filters while preserving factual accuracy, descriptive depth, and logical reasoning.

• High-Fidelity Reasoning and Visual Understanding Generates detailed captions and structured reasoning for diverse visual categories—artistic, technical, abstract, or low-context.

• Enhanced Supervised Fine-Tuning (SFT) Alignment Trained on a meticulously curated dataset via HoneyPipe with short and long Chain-of-Thought (CoT) annotations, ensuring deep reasoning coherence.

• Aspect-Ratio Robustness Performs consistently across wide, tall, square, panoramic, and irregular visual formats.

• Variational Detail Control Supports both concise summaries and highly detailed reasoning narratives, depending on prompt configuration.

• Multilingual Output Capability Defaults to English but adaptable for multilingual use through prompt engineering.

Quick Start with Transformers

import requests
import torch
from PIL import Image
from transformers import AutoModel, AutoProcessor

model_path = "prithivMLmods/Bee-8B-SFT-abliterated"

# Load model
model = AutoModel.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).to("cuda")

# Load processor
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

# Define conversation messages
messages = [{
    "role":
    "user",
    "content": [
        {
            "type": "image",
            "image": "https://huggingface.co/Open-Bee/Bee-8B-SFT/resolve/main/assets/logo.png",
        },
        {
            "type": "text",
            "text": "Based on this picture, write an advertising slogan about Bee-8B (a Fully Open Multimodal Large Language Model)."
        },
    ],
}]

# Apply chat template
text = processor.apply_chat_template(messages,
                                     tokenize=False,
                                     add_generation_prompt=True,
                                     enable_thinking=True)

# Load image
image_url = "https://huggingface.co/Open-Bee/Bee-8B-SFT/resolve/main/assets/logo.png"
image = Image.open(requests.get(image_url, stream=True).raw)

# Process inputs
inputs = processor(images=image, text=text, return_tensors="pt").to("cuda")

# Generate output
generated_ids = model.generate(**inputs, max_new_tokens=16384, temperature=0.6)
output_ids = generated_ids[0][len(inputs.input_ids[0]):]

# Decode output
output_text = processor.decode(output_ids, skip_special_tokens=True)

# Print result
print(output_text)

Intended Use

This model is suited for:

• Generating detailed, uncensored captions and reasoning for complex or creative visual datasets.
• Research in multimodal reasoning, safety evaluation, and content moderation studies.
• Enabling descriptive captioning and analytical reasoning for datasets excluded from mainstream models.
• Creative applications such as narrative generation, artistic interpretation, and visual storytelling.
• Advanced reasoning over diverse visual structures and aspect ratios.

Limitations

• May produce explicit, sensitive, or offensive content depending on input and prompt.
• Not recommended for deployment in production systems requiring strict moderation or filtering.
• Style, tone, and reasoning detail may vary based on prompt phrasing.
• May show variable performance on synthetic, abstract, or highly stylized visual inputs.